Decentralised clinical trials (DCTs) have emerged as a promising way to increase patient retention and compliance and decrease recruitment times, ultimately shortening trial times and saving money overall.
For chronic diseases, including diabetes, where patients are healthy enough to do the monitoring on their own, there has been great enthusiasm for decentralised trials.
But even with vast promise, the less rigid structure that decentralised trials allow for has also created new problems that researchers and healthcare professionals must solve – one of the biggest being the legislative question.
“There’s a lot of insecurity in terms of what is allowed and what isn’t, especially in Europe,” says Dr. Benedikt Linder, Scientific Consultant at Germany-based Alcedis, which is owned by UK-based healthcare company Huma. “Every country has its own sub-regulations, which makes it quite complicated.”
There is movement towards standardising principles, and at the end of 2022, the European Medicines Agency (EMA) and Heads of Medicines Agency (HMA) published a paper issuing non-binding guidance on the use of decentralised elements in clinical trials in the European Union and the European Economic Area.
Another challenge is building trust. Because the concept is so novel, there’s a certain public fear around decentralised trials, Dr. Linder says.
“One way to tackle this is by focusing on patient centricity and demonstrating how these trials can save time for the investigators and patients. Doctors and nurses want to work with the patients and not waste time documenting things twice or thrice,” Dr. Linder says.
Saving doctors’ time
Healthcare professionals spend an average of 13.5 hours a week on clinical documentation, according to a 2022 study from Nuance and research consultancy Ignetica that tracked workers time in the United Kingdom.
Data transfer between electronic health records and electronic data capture (EDC) systems for clinical research is typically carried out manually – a process that is error-prone, cost-intensive and time-intensive.
A team of German experts has developed a model for automated data transfer that allows data to be captured at the point of care, extracted from various EMRs, such as hospital or practice systems, and automatically transferred to electronic case report forms in EDC systems. They published their findings alongside the new model in a 2023 study titled Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis.
“It is anticipated that this reduced documentation burden may promote the willingness of health care professionals to participate in clinical studies,” the study’s authors write.
An automated model
One of those authors, Alcedis managing director Dr. Bernhard Remes, says that decentralised trials create more time for physicians by extracting data directly from electronic health records.
To reduce the time spent documenting, Alcedis has created a process, where patient data is recorded and transferred automatically to the EDC system, eliminating the need for manual – and often error-prone – data transfer.
“Everything the investigator documents is transferred to the eCRF in real-time, and it’s all stored there so the investigator doesn’t have to document it twice,” says Dr. Remes.
Where a typical EDC or eCRF system can’t cope with large amounts of unstructured data, the Alcedis eCRF is customisable so it only captures the data investigators need. For example, it can prioritise events-based data logging so researchers aren’t inundated with copious amounts of irrelevant data.
“For example: Investigators can monitor clinically relevant changes in blood pressure directly in the eCRF and they’re notified as well on the corresponding dashboard, but it’s all managed in one application,” Dr. Remes says.
By keeping all the data in one system, investigators don’t have to switch between applications, eliminating a key concern in digitalisation processes.
The Alcedis eCRF reduces up to 80% of manual data cleaning efforts and comes with a powerful reporting toolbox that is customised and can be effortlessly configured. The eCRF also uses cutting-edge methods and technologies like machine learning and artificial intelligence to support recognition of hidden adverse events or missing critical data, anomaly detection, intelligent coding (term-recognition) and forecast modelling (e.g. recruitment, potential analysis by imputation) or pre-diagnostic.
To learn more about how new technologies support the decentralisation of clinical trials, download the free whitepaper here.
